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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Child Neuropsychol. 2021 May 2;27(8):1054–1072. doi: 10.1080/09297049.2021.1917531

The Role of Distinct Executive Functions on Adaptive Behavior in Children and Adolescents with Down syndrome

Elizabeth A Will 1, Emily K Schworer 2, Anna J Esbensen 2,3
PMCID: PMC8484022  NIHMSID: NIHMS1701268  PMID: 33938385

Abstract

Difficulties in executive function are a relatively well-characterized feature of the neuropsychological profile in Down syndrome (DS), yet the impact of these challenges on aspects of daily functioning remain poorly understood. We examined the role of specific executive functions on domains of adaptive behavior in children and adolescents with DS. Participants included 68 children and adolescents with DS between 6 – 17 years old (mean chronological age = 12.56 years; SD = 3.22) and their caregivers. Parent reported executive function skills were measured using the BRIEF-2 and adaptive behavior was measured using the Vineland Adaptive Behavior Scales-III. Results identified working memory as a significant predictor of Communication, Daily Living, and Socialization skills, and Shifting significantly predicted Daily Living and Socialization. Findings demonstrate the relation between executive functions and adaptive behavior and highlight the effects of working memory on aspects of daily functioning for individuals with DS.

Introduction

Down syndrome (DS), the leading known genetic cause of intellectual disability (ID), is characterized by phenotypic areas of strength and challenge relative to developmental level. One particular area of research interest within the DS behavioral phenotype is executive function (EF), a collection of interrelated cognitive processes that facilitate daily functioning (Diamond, 2013; Silverman, 2007). The past ten years have seen a substantial amount of progress in identifying areas of particular difficulty and ability commensurate with developmental status across EFs at various points of development in DS (Daunhauer et al., 2014; Tungate & Conners, 2021; Loveall et al., 2017; Lee et al., 2015; Lanfranchi et al., 2010). Further, we have begun to ascertain the functional impact of specific areas of difficulty in EF on various outcomes in DS including language acquisition (Baddeley & Jarrold, 2007), academic achievement (Will et al., 2017), and school-participation (Daunhauer et al., 2014). Although we have a relatively thorough understanding of how EFs contribute to the execution of daily skills and adaptive engagement in typical development (Blair & Razza, 2007; Diamond, 2013), less is known regarding the impact of specific EFs on aspects of daily adaptive functioning in DS. As such, we aim to identify the relation between specific EFs and domains of adaptive functioning to gain a more thorough understanding of the possible functional consequences of impaired EFs in DS and potentially cultivate knowledge for intervention targets.

Executive function in Down syndrome

Executive function is a set of dissociable yet interrelated cognitive processes that facilitate processing of information necessary to engage in goal-directed behavior (Diamond, 2013; Miyake et al., 2000; Tungate & Conners, 2021). The cognitive components most commonly considered as EFs include inhibition, shifting (i.e., cognitive flexibility or attention shifting), and working memory (Diamond, 2013; Miyake et al., 2000; Tungate & Conners, 2021). These components work together to facilitate higher-order cognitive skills such as goal-directed behavior, yet can be measured distinctively from one another (Diamond, 2013; Miyake et al., 2000; Miyake & Friedman, 2012). Broadly speaking, EFs are considered to be an area of distinct challenge for individuals with DS (Lanfranchi et al., 2010; Lee et al., 2011) relative to individuals with other genetic disorders associated with ID (Carney et al., 2013; Costanzo et al., 2013; Rowe et al., 2006) and individuals with typical development (Daunhauer et al., 2014; Lanfranchi et al., 2010). When considering components of EF individually, however, specific EFs appear to be more impaired relative to other areas (Daunhauer et al., 2014; Lee et al., 2011, 2015; Tungate & Conners, 2021).

Inhibition.

One primary EF is inhibition, which refers to the ability to suppress a prepotent response. Evidence on inhibition skills in DS is somewhat mixed. In children and adolescents with DS, inhibition emerges as an area of challenge compared to matched groups with ID without DS and typical controls of the same developmental level (Costanzo et al., 2013; Borella et al., 2013). Yet, other findings show that inhibition is commensurate with developmental level for adolescents with DS (Carney et al., 2013; Lanfranchi et al., 2010), both in the context of ID and TD matched comparisons. In a recent meta-analysis, inhibition was confirmed as an area of difficulty in DS compared to matched typically developing controls but was also confirmed to be less impaired relative to the other primary EFs, such as shifting and working memory (Borella et al., 2013; Carney et al., 2013; Costanzo et al., 2013; Lanfranchi et al., 2010; Tungate & Conners, 2021).

Shifting.

Shifting, a second primary component of EF, is defined as the ability to efficiently and effectively transition from one task or set of rules to another (Zelazo, 2006). Evidence on shifting abilities in DS is also somewhat mixed (Tungate & Conners, 2021). Shifting is characterized as an impaired EF in children and adolescents with DS relative to matched typically developing controls (Costanzo et al., 2013; Lanfranchi et al., 2010). This pattern has been replicated in adolescents and adults with DS compared to other individuals with ID of the same developmental or receptive language level (Phillips et al., 2014; Rowe et al., 2006). Results from a recent meta-analysis (Tungate & Conners, 2021) on EF in DS provides support for shifting as an impaired EF in DS; however, only studies employing laboratory-based measures of EF abilities were included. Contrary to these findings, results from studies focused on parent-reported EFs suggest that shifting may be an area of strength relative to developmental status (Daunhauer et al., 2014) as well as other EF domains (Lee et al., 2011; Loveall et al., 2017).

Working Memory.

Working memory, a third primary EF, is a cognitive system used to store, process, and update information (Baddeley & Hitch, 1974; Baddeley & Jarrold, 2007). This system can be dissociated into two separate modalities – visual working memory and verbal working memory (Baddeley & Jarrold, 2007; Jarrold et al., 1999; Lanfranchi et al., 2012; Numminen et al., 2001), both of which contribute to higher-order goal-directed behavior. Working memory is well researched in DS and has been characterized as an area of relative difficulty, regardless of whether measured by a lab-based task, parent report, or teacher report (Conners et al., 2011; Daunhauer et al., 2014, 2017; Lee et al., 2015). Although individuals with DS perform slightly better on visual working memory tasks relative to auditory working memory tasks (Lanfranchi et al., 2004, 2012; Næss et al., 2011), performance in both of these areas lags behind typically developing peers with the same developmental level (Borella et al., 2013; Carney et al., 2013; Costanzo et al., 2013; Lanfranchi et al., 2012). In addition, individuals with DS show greater impairments in working memory abilities relative to other individuals with ID (Carney et al., 2013; Costanzo et al., 2013; Rowe et al., 2006) suggesting a specific DS EF profile, although this is somewhat dependent on whether the task modality is verbal or visual (Kittler et al., 2006; Rowe et al., 2006).

Implications for executive impairments

As a collection of interrelated cognitive skills, these primary EFs (i.e., inhibition, shifting, and working memory) coalesce to facilitate goal-directed behavior/planning (Lanfranchi et al., 2010; Miyake et al., 2000). Planning is considered a marked area of difficulty in EF for individuals with DS. It emerges with a more significant degree of impairment relative to other domains on parent reported EFs (Daunhauer et al., 2014; Lee et al., 2011, 2015; Loveall et al., 2017), and individuals with DS show challenges with planning on lab-based tasks compared to matched comparison groups (Fidler et al., 2014; Rowe et al., 2006).

Impairments in any specific primary domain of EF, or in higher-order EF (e.g., planning) as the pinnacle performance of the primary domains, have important implications for a variety of outcomes. For instance, the ability to successfully inhibit impulsive behaviors has a significant impact on optimal functioning and participation in the school setting for children with DS (Daunhauer et al., 2014). Difficulty with shifting can lead to missed learning opportunities or hyperfocus on irrelevant stimuli, which may translate to off task and oppositional behavior around transitions (Will et al., 2016). Further, working memory is critical in the development and use of language and reading development (Abbeduto et al., 2007; Jarrold et al., 2009; Næss et al., 2011; Yang et al., 2014), academic achievement (Will et al., 2017), and employment (Tomaszewski et al., 2018) for individuals with DS. Considering that planning is a higher-order EF reliant on these specific subdomains, difficulty in this area is likely to manifest across a variety of the domains affected by difficulties in inhibition, shifting, and working memory. Despite some understanding of the functional consequences related to executive impairment in DS, adaptive behavior is another domain likely affected by EFs. However, the specific association between these phenotypic features in DS remains unclear.

Adaptive behavior

Adaptive behavior is an area of functioning critical for engaging in conceptual, practical, and social aspects of daily life (Ditterline & Oakland, 2009; Schalock et al., 2010; Tassé et al., 2016). Measurement of adaptive behavior typically encompasses multiple domains of functioning, including communication, daily living skills, and socialization. A proficient adaptive repertoire is associated with higher academic achievement (Bornstein et al., 2013), reduced levels of maladaptive behaviors (Racz et al., 2017), and increased independence in adulthood (Woolf et al., 2010). In general, adaptive skills are an area of marked difficulty in DS (see Daunhauer, 2011 for review), and children with DS show significantly reduced adaptive skills relative to chronological (Will et al., 2018) and developmental expectations (Fidler et al., 2006). However, similar to EFs, children with DS express a profile consisting of proficiencies as well as challenges across the various domains of adaptive functioning (Daunhauer, 2011; Fidler et al., 2006; Van Duijn et al., 2010). In studies drawing within-group comparisons across adaptive domains, socialization (Dykens et al., 1994; Fidler et al., 2006; Marchal et al., 2016), and in some cases, daily living skills (Dykens et al., 1994) emerge as stronger skills relative to communication abilities.

Along with a well-characterized adaptive profile in DS, there is also an understanding of certain mechanisms contributing to adaptive impairments. Specifically, attentional difficulties (Jacola et al., 2014), visual-motor integration (Rihtman et al., 2010), and repetitive behaviors (Evans et al., 2014) have all been found to predict adaptive abilities in children with DS. Yet, there is minimal evidence characterizing the relation between specific EFs and adaptive skills in DS, with, to the best of our knowledge, only one other study examining this association (Daunhauer et al., 2017). This particular study examined effects of isolated EFs on self-care skills and found parent reported working memory abilities to predict self-care outcomes (Daunhauer et al., 2017). While these findings provide useful insight into the potential association of EF to adaptive skills, they are still somewhat limited in that only one primary EF (i.e., working memory) was tested as a predictor of only one domain of adaptive functioning (i.e., self-care). A more complete understanding of how multiple EFs relate to the various facets of adaptive behavior can yield insight into optimal intervention targets and strategies to improve adaptive outcomes in DS.

Current study & aims

Given the preliminary findings associating EF and daily living skills, it is imperative to understand how specific areas of cognitive impairment, such as EFs, influence domains of adaptive functioning in DS, especially as individuals with DS experience elevated risk for vulnerabilities in both cognitive and adaptive skills. As such, we aim to address this significant knowledge gap by examining the associations of isolated EFs on domains of adaptive behavior. Specifically, we utilize a parent report EF measure to identify significant EF predictors of communication, daily living, and socialization skills in children and adolescents with DS. The primary aim of the study was to characterize the relation of parent reported EFs to parent reported adaptive skills and identify which aspects of EF are the most salient predictors of adaptive behavior. Working memory and planning have been consistently established as impaired EFs, and, considering their relation to self-care skills (Daunhauer et al., 2017), we anticipate these EFs to significantly predict daily living skills in DS. Further, because working memory is arguably the most significantly affected EF in DS (Baddeley & Jarrold, 2007; Daunhauer et al., 2014; Lanfranchi et al., 2010, 2012; Will et al., 2017), we also anticipate that this EF will emerge as a salient predictor across adaptive skills. In post hoc analyses, we considered whether attention may moderate the association between EFs and adaptive skills, given that attention is a foundational component for higher order cognitive processes (Diamond, 2013) and relatively impaired in DS (Ekstein et al., 2011; Jacola et al., 2014; Will et al., 2016).

Methods

Participants

Table 1 presents full participant characteristics. Participants included 68 children with DS between 6 and 17 years old (CAm = 12.56; IQm=43.83). The sample was relatively evenly split across sex (53% male), but primarily Caucasian (88.2%; 3.0% African American; 4.4% Asian; 4.4% Other) and non-Hispanic (92.6%). Data from the participants included in the current study is also reported in two other studies focusing on the psychometric evaluation of neuropsychological measures in DS (Schworer et al., 2021). Five individuals who participated in the study were excluded from analyses because they were missing one or more of the parent report questionnaires and analyses required complete data for those study measures.

Table 1.

Participant characteristics and mean scores for measures of executive function, adaptive behavior, and ADHD symptomatology, n = 68.

Mean (SD) Minimum Maximum
Chronological age 12.56 (3.22) 6 17
SB-5 ABIQ 48.83 (4.91) 47 76
BRIEF-2 (T scores)
 Inhibit 56.25 (10.46) 37 87
 Self-Monitor 61.99 (9.41) 39 80
 Shift 61.82 (11.11) 44 87
 Emotional Control 51.49 (9.28) 40 74
 Initiate 61.63 (10.77) 39 90
 Working Memory 62.60 (8.85) 42 89
 Plan/Organize 57.38 (9.83) 37 80
 Task-Monitor 61.34 (8.93) 38 77
 Organization of Materials 53.99 (8.79) 38 88
VABS-III ABC standard score 68.60 (10.87) 29 95
 Communication standard score 64.19 (15.89) 20 91
 Daily Living standard score 66.57 (14.32) 32 104
 Socialization standard score 74.10 (13.20) 27 98
Vanderbilt Inattention Sum 11.39 (4.80) 1 23
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SB-5 Deviation ABIQ = The Stanford-Binet Intelligence Scales – Fifth Edition abbreviated battery IQ; BRIEF-2 = Behavior Rating Inventory of Executive Function – Second Edition; VABS-III ABC= Vineland Adaptive Behavior Scales – 3rd Edition Adaptive Behavior Composite

Procedures

Procedures for this multi-site study were approved by the Streamlined, Multisite, Accelerated Resources for Trials (SMART) IRB platform. Participants were recruited through local DS organizations and a medical center to take part in a larger longitudinal study on cognition in DS. To be eligible for the study, participants were required to speak English as their primary language and have a parent reported nonverbal mental age of approximately 36 months to compete neuropsychology assessments in the larger longitudinal study. Documented diagnosis of DS was also required for participation. Study visits took place at a medical clinic or university laboratory located in midwestern and western US cities. Data from the first visit of the longitudinal study were used in the current study. Parents of participants provided written consent and filled out questionnaires on their child’s daily EF skills, adaptive behavior, and ADHD symptomatology. Participants engaged in a larger battery of neuropsychological assessments, including an assessment of IQ for the purposes of this study.

Measures

Cognitive Ability.

The Stanford-Binet Intelligence Scales – Fifth Edition (SB-5; Roid, 2003) was used as a measure of IQ to describe the participant sample. The SB-5 is a standardized measure of intelligence and the abbreviated battery IQ (ABIQ) was administered, which includes one nonverbal subtest (Fluid Reasoning) and one verbal subtest (Knowledge). Reliability for the ABIQ is high (r = 0.85 – 0.96) and correlations are strong between the ABIQ and full-scale IQ (r = 0.89) in other clinical samples (Twomey et al., 2018). Three participants were missing the SB-5 ABIQ.

Executive Functions.

Executive function skills were measured using the Behavior Rating Inventory of Executive Function – Second Edition (BRIEF-2; Goia et al., 2015). The BRIEF-2 is a standardized parent or teacher report measure of daily EF skills consisting of 63 items which are rated on a 3-point likert scale indicating how frequently a child engages in a given behavior: never (0), sometimes (1), or often (2); thus, higher scores indicate greater EF difficulties. The parent-report BRIEF-2, used in the present study, measures EF skills across three indices: Behavior Regulation (Inhibit, Self-Monitor), Emotion Regulation (Shift, Emotional Control), and Cognitive Regulation (Initiate, Working Memory, Plan/Organize, Task-Monitor, Organization of Materials). The BRIEF-2 parent form has high internal consistency (α = 0.76 – 0.97) and stable test-retest reliability (r = 0.67 to 0.92). Previous research establishes the BRIEF (largely similar to the BRIEF-2) as an appropriate measure of EF in children and adolescents with DS (Esbensen et al., 2019). For the purposes of this study, index sub-domain T scores (M = 50; SD = 10) were used as predictors of adaptive behavior outcomes to determine the isolated effect of specific EFs on adaptive behavior domains. This approach provided predictive value for each primary EF – Inhibit, Shift, and Working Memory – as well as their proximally associated EFs (i.e., Self-Monitor, Emotional Control, Initiate, Plan/Organize, Task-Monitor, and Organization of Materials).

Adaptive Behavior.

Adaptive behavior was measured using the Vineland Adaptive Behavior Scales – 3rd Edition (VABS-III; Sparrow et al., 2016) Parent/Caregiver Form. The VABS-III caregiver form is a parent report questionnaire that comprehensively measures adaptive behavior across four domains including Communication, Socialization, Daily Living Skills, and Motor Skills. Items are scored on an ordinal scale which indicates the frequency at which the individual independently performs a skill – usually (2), sometimes (1), or never (0). The VABS-III provides domain standard scores and an overall composite score, the Adaptive Behavior Composite, each of which have a mean of 100 and standard deviation of 15. Caregiver form test-retest reliability (r = 0.64 – 0.94) and internal consistency (α = 0.95 – 0.99) are high and the VABS-III is suitable for assessing adaptive skills in DS (Esbensen et al., 2017). Due to availability of data, the Communication, Socialization, and Daily Living scores were used in analyses and all analyses used standard scores.

Inattention.

The Vanderbilt ADHD Parent Rating Scale (Wolraich et al., 2003) provided a measure of inattention which was used in post-hoc moderation analyses (see Data Analysis) to determine whether significant associations between domains of EF and AB varied as a function of attentional difficulties. The Vanderbilt ADHD includes domains across Inattention, Hyperactivity, Conduct/oppositional, and Anxiety/Depression. Parents rate ADHD symptoms on a 4-point Likert scale of 0 to 3 which indicates whether the symptom occurs: never (0), occasionally (1), often (2), or very often (3) and assesses level of functional impairment as a result of symptoms. The measure is psychometrically sound, with high internal consistency (alpha > 0.80), reliability and validity (r > 0.80) and has been recommended for use in individuals with ID (Esbensen et al., 2017). For the purposes of this study, the summed score of the nine inattention items was used as an indicator of inattention.

Data Analyses

All data were examined for normality of distribution and descriptive statistics were computed (see Table 1). Preliminary bivariate correlations were tested to determine the magnitude of correlation between IQ and BRIEF-2 index subdomains, and IQ and adaptive behavior domains, respectively. As anticipated, IQ was moderately correlated with all adaptive domains (rs = 0.25 – 0.36; p values = .003 - .045). However, IQ was not significantly correlated with any EF subdomains on the BRIEF-2 (rs = −0.14 – 0.23; all p values > .05) and was therefore determined to not be a potential confound of the association between EFs and adaptive behavior. We also evaluated CA as a potential covariate, but no significant correlation was identified between CA and any EF index subdomain (r = −.21 - .12; all p-values > .05). Given intercorrelations among BRIEF-2 index subdomains (r = .29 - .73; p-values<.001), we elected to use multivariate regression models. This modeling approach accounts for potential issues of multicollinearity by yielding the isolated effects of a single independent variable on a single outcome (Johnson & Wichern, 2007). To maximize statistical power, three separate multivariate models were estimated in accordance with the three BRIEF-2 index domains: 1) a Behavior Regulation model, which included Inhibit and Self-Monitor subdomains as predictors; 2) an Emotion Regulation model that included Shift and Emotional Control subdomains as predictors; and 3) a Cognitive Regulation model, which included Initiate, Working Memory, Plan/Organize, Task-Monitor, and Organize Materials subdomains as predictor variables. In each of these models, Communication, Daily Living, and Socialization standard scores were regressed on each set of predictors associated with the BRIEF-2 Indices (i.e., Behavior, Emotion, and Cognitive Regulation). Using a multivariate regression approach enabled us to determine the unique predictive value and variance accounted for (ηp2) by each specific EF on each domain of adaptive functioning. Further, effect sizes (ηp2) provided information on the salience of each EF for each adaptive domain and enabled comparisons across EFs that were not included in the same model.

Finally, significant associations identified between specific EF index subdomains and adaptive domains prompted post-hoc analyses of the effect between specific EFs and adaptive domains being moderated by degree of inattention as measured by the Vanderbilt ADHD parent rating form. We estimated post-hoc moderated regression models to test whether the effect of EF on adaptive behavior varied as a function of inattention. Results are outlined below.

Results

Behavior Regulation Index

The first multivariate regression model tested effects of Inhibit and Self-Monitor on adaptive behavior domains outcomes. Results showed no significant findings of either Inhibit or Self-Monitor as a predictor of Communication, Socialization, or Daily Living adaptive domains (see Table 2).

Table 2.

Multivariate Regression Model Results

Behavior Regulation Model
Communication
 Daily Living
 Socialization
B SE(b) p ηp 2 B SE(b) p ηp 2 B SE(b) p ηp 2
Intercept 82.01 13.89 <.001 .35 96.12 12.08 <.001 .49 107.53 10.90 <.001 .60
Inhibit −0.14 0.21 .503 .01 −0.30 0.18 .104 .04 −0.27 0.17 .107 .04
Self- Monitor −0.16 0.23 .501 .01 −0.20 0.20 .325 .02 −0.29 0.18 .115 .04
Adjusted R2<0.01 Adjusted R2=0.07 Adjusted R2=0.11
Emotion Regulation Model
Communication
 Daily Living
 Socialization
B SE(b) p ηp 2 B SE(b) p ηp 2 B SE(b) p ηp 2
Intercept 86.64 12.53 <.001 .42 90.45 11.03 <.001 .51 104.01 9.90 <.001 .63
Shift −0.27 0.20 .180 .03 −0.41 0.17 .023 .08 −0.41 0.16 .011 .10
Emotional Control −0.11 0.24 .631 .00 0.02 0.21 .916 .00 −0.09 0.19 .626 .00
Adjusted R2=0.02 Adjusted R2=0.07 Adjusted R2=0.12
Cognitive Regulation Model
Communication
 Daily Living
 Socialization
B SE(b) p ηp 2 B SE(b) p ηp 2 B SE(b) p ηp 2
Intercept 74.85 14.55 <.001 .30 84.18 13.34 <.001 .39 103.90 11.42 <.001 .57
Initiate −0.19 0.27 .476 .01 −0.33 0.25 .188 .03 −0.27 0.21 .208 .03
Working Memory −1.06 0.34 .003 .14 −0.69 0.31 .030 .07 −0.94 0.27 .001 .17
Plan/Organize 0.30 0.28 .290 .02 0.36 0.26 .164 .03 0.24 0.22 .288 .02
Task-Monitor 0.62 0.32 .056 .06 0.21 0.29 .473 .01 0.38 0.25 .131 .04
Organization of Materials 0.23 0.27 .405 .01 0.22 0.25 .392 .01 0.16 0.22 .459 .01
Adjusted R2=0.13 Adjusted R2=0.10 Adjusted R2=0.22
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Emotion Regulation Index

The second multivariate regression model tested effects of Shift and Emotional Control as predictors of Communication, Daily Living, or Socialization skills. Overall model results indicated that Emotion Regulation features (i.e., Shift and Emotional Control) accounted for a significant portion of variance in Daily Living (AdjR2 = 0.07) and Socialization (AdjR2 = 0.12), and also revealed a significant overall effect for Shift and adaptive behavior (F(3, 63) = 3.41; p = .023; ηp2 = 0.14). Individual parameter estimates indicated that Shift significantly predicted both Daily Living (B = −0.41; p = .023; ηp2 = 0.08), and Socialization (B = −0.41; p = .011; ηp2 = 0.10). Effect size estimates showed that Shifting accounted for approximately 8% of unique variance in Daily Living (ηp2 = 0.08) and 10% of the unique variance in Socialization (ηp2 = 0.10). Each was a moderate effect, and trends indicated that greater impairments in Shift predicted lower Daily Living and Socialization skills. Shift was not found to significantly predict Communication (B = −0.27; p = .180). In addition, Emotional Control was not identified as a significant predictor of any adaptive behavior domains (see Table 2).

Cognitive Regulation Index

The third multivariate regression model tested effects of Initiate, Working Memory, Plan/Organize, Task-Monitor, and Organization of Materials on Communication, Daily Living, or Socialization skills. Overall model results indicated that Cognitive Regulation features accounted for a significant portion of the variance in Communication (AdjR2 = 0.13), Daily Living (AdjR2 = 0.10), and Socialization (AdjR2 = 0.22). Model results also showed an overall significant effect for Working Memory on adaptive behavior domains (F(3,60) = 4.32; p = .008). Individual parameter estimates indicated that main effects were primarily driven by Working Memory, with significant effects on all three domains of adaptive behavior. Specifically, Working Memory was found to significantly predict Communication (B = −1.06; p = .003; ηp2 =0.14), Daily Living (B = −0.69; p = .030; ηp2 =0.07), and Socialization (B = −0.94; p = .001; ηp2 =0.17). Effect size estimates indicated that Working Memory accounted for approximately 14% of the unique variance in Communication (ηp2 = 0.14), about 7% of the unique variance in Daily Living Skills (ηp2 = 0.07), and 17% of the unique variance in Socialization (ηp2 = 0.17). Each of these were moderate-to-large effects, and trends indicated that lower Working Memory predicted poorer adaptive functioning in each domain. No other significant effects were identified between Initiate, Plan/Organize, Task-Monitor, and Organization of Materials and adaptive behavior domains (see Table 2).

Post hoc moderation analyses

Following our multivariate regression analyses, an additional question arose: Does the association between EF and adaptive behavior vary as a function of attentional difficulties? To address this post-hoc question, we tested additional moderated regression models for each significant effect between EFs (i.e., Shift or Working Memory) and Communication, Daily Living, or Socialization. Each moderated regression model included the EF predictor, inattention, and an EF x Inattention interaction term. Results showed no main effects for inattention as a predictor of any adaptive domain; however, trends towards statistical significance were identified for the association between inattention and Communication (b=1.03; p=.078) accounting for Working Memory, and between inattention and Daily Living (b=1.00; p=.061) accounting for Working Memory. In addition, model results showed that inattention did not significantly moderate the effect of Shifting on Daily Living or Socialization, or of Working Memory on Communication, Daily Living, or Socialization. Interestingly, Shift remained a significant predictor of Daily Living (b = −0.44; p=.011) and Socialization (b = −0.36; p = .019) even when accounting for inattention. Likewise, Working Memory also remained a significant predictor of Communication (b= −0.83; p = .011), Daily Living (b = −0.88; p = .003), and Socialization (b = −0.72; p = .006) when accounting for inattention.

Discussion

This study presents new findings regarding the influence of specific EF components on adaptive behavior domains in children and adolescents with DS. Shifting and working memory, two of the three primary EFs, were related to several aspects of adaptive functioning. BRIEF-2 Working Memory emerged as the most salient predictor across each model, related to all three adaptive domains – Communication, Daily Living Skills, and Socialization – and demonstrating the largest effect sizes. Although attentional capacities fundamentally support executive skills, inattention was not found to significantly moderate effects between EF components and adaptive domains in our post hoc analyses. Interestingly, no other EFs, primary or proximal, on the BRIEF-2 significantly related to domains of adaptive skills. Collectively, these findings are relatively consistent with prior work on EF in DS, and they provide insight into the nature and association between EF and adaptive skills that can inform targets for intervention.

Shifting and adaptive skills

Shifting represents the ability to flexibly focus attention and cognitive effort on relevant stimuli. Generally regarded as an impaired EF in DS (Tungate & Conners, 2021), shifting abilities tend to decrease across middle childhood and adolescence in DS (Loveall et al., 2017). Along with our findings, this suggests increasingly impaired shifting abilities have a functional impact on multiple adaptive skills for children and adolescents with DS. Specifically, our results show that greater shifting impairments predict lower adaptive functioning in both daily living and socialization skills. Daily living skills like dressing, bathing, and taking medication, naturally require the ability to flexibly reorient attention as various steps of the task are carried out, or as modifications to the task are required. Whereas other predictors of daily living abilities have been identified in DS (Daunhauer et al., 2017), our results provide new evidence that shifting is another EF related to successful daily adaptation.

Our findings also highlight a relation between shifting and socialization skills in DS, and, more specifically, showed that greater difficulties in shifting were associated with lower socialization skills. Within a social context, shifting is required to hold a conversation that may transition across various topics, or to maintain interaction across multiple social partners. Thus, our findings suggest that the shifting difficulties experienced by children with DS may affect the complexity of their social interactions and consequently hinder the development of more advanced social skills and/or social relationships in later development. Shifting difficulties are shown to increase in DS at the same developmental period in which social demands increase – adolescence (Loveall et al., 2017). It may be the case that as social expectations increase with age and development, demands exceed social capabilities of older children and adolescents with DS due to their challenges with cognitive shifting.

Working Memory – a highly salient EF

Our findings identified working memory as the most salient EF associated with adaptive behavior in that it was associated with all three domains of adaptive functioning and demonstrated the largest effects with communication and socialization skills. A considerable amount of work has unequivocally established working memory as an impaired EF in DS (see Tungate & Conners, 2021 for review), and characterized its role for reading abilities and academic achievement in DS (Lemons & Fuchs, 2010; Will et al., 2017). Our specific result that greater difficulties in working memory were associated with lower daily living skills is consistent with prior evidence for longitudinal associations between working memory and self-care skills in older children with DS (Daunhauer et al., 2017). Taken together, these findings underscore the cognitively taxing nature of daily living skills, which often require holding a substantial amount of information or tasks in mind while completing various steps (e.g., dressing), in the context of working memory challenges such as those characterized in DS.

While the relation between working memory and the concrete multi-step skills involved in executing daily living activities may be clearer, its role in communication and socialization skills is perhaps slightly more nuanced. Both communication and socialization skills require real-time updating of stored contextual cues and active recall of temporarily stored information. As such, proficient working memory abilities are necessary to successfully understand and produce language and communicative behaviors in real-time to effectively convey needs and participate in conversations, as well as navigate social contexts. The particularly strong effects between working memory and these adaptive domains suggests that the pronounced working memory difficulties associated with the DS phenotype likely have more extensive functional consequences than previously considered.

Implications for Socialization

Of particular note, the largest overall effect size and the greatest amount of variance accounted for in an adaptive domain was driven by working memory and its effect on socialization skills. This finding is consistent with prior work in which working memory was found to account for over 50% of the variance in social cognition in a sample of 6 – 12-year-old children with DS (Amadó et al., 2016). These collective findings, along with our result that shifting difficulties were associated with lower socialization skills, provide insight into features of the DS behavioral phenotype. Specifically, social abilities have long been regarded as an area of strength in the DS phenotype (Daunhauer, 2011; Fidler et al., 2008). This characterization is firmly grounded in evidence from toddlerhood in DS, showing strong social interest (Kasari & Freeman, 2001) and social abilities commensurate with developmental expectations (Fidler et al., 2008). Further, when examining within-group adaptive performance, socialization is often the least affected domain when compared to others for children with DS (Dykens et al., 1994; Fidler et al., 2006).

However, it may be the case that social abilities in DS are more affected than previously understood, or more affected at certain developmental periods than others. Much of the evidence demonstrating strong socialization in DS has emerged from early developmental periods or from comparisons to inherently less social groups, such as autism spectrum disorder (Cebula et al., 2010). The degree of shifting and working memory difficulties identified in DS (Baddeley & Jarrold, 2007; Daunhauer et al., 2014; Tungate & Conners, 2021) paired with presented and previous findings that establish a link to socialization suggests these EFs likely hinder effective socialization in later childhood and adolescence. Children and adolescents with DS may be able to meet basic expectations in a social context, such as mirroring body language, identify emotions expressed by a conversational partner, or follow subtle social cues, but challenges in both shifting and working memory are likely to result in challenges or even a full inability to effectively coordinate all the skills necessary for an effective social interaction. In typical development, shifting emerges during preschool ages, whereas for working memory, basic abilities to store information begins in infancy and progresses towards more complex manipulation across later childhood (Diamond, 2013). As such, significant and increasing (Loveall et al., 2017) challenges in these EFs in DS are likely to also impact the development of, or perhaps, the ability to maintain, proficient socialization skills across time.

Implications for other EFs

Somewhat unexpectedly, no other EF components were identified as significant predictors of adaptive domains in the present study. Inhibition, though impaired in DS relative to typical development, is a lesser impaired EF relative to both shifting and working memory (Tungate & Conners, 2021); this profile of impairment may explain the lack of association between inhibition and the adaptive domains we examined. Higher-order EF (e.g., Plan/Organize, Task-Monitor, and Organization of Materials), on the other hand, is well established as particularly impaired in DS (Daunhauer et al., 2014; Fidler et al., 2014; Lee et al., 2011). Additionally, as the evident culmination of primary EF components, planning is seemingly integral to successful adaptation, and reasonably so across communication, daily living, and socialization skills. However, our results did not support a significant relation between planning and any domain of adaptive behavior we examined. This was also true of proximal EF components, such as task monitoring or organization of materials. These findings may indicate that adaptive behavior measures potentially decentralize skills into incremental parts, thus failing to capture the full skill expression that higher-order EFs would require. Alternatively, it could be the case that because primary EFs are foundational, they have a greater association to adaptive skills.

Effects of Attention on EF-Adaptive associations

Attention is a core component of EF as focused attention facilitates other primary EFs (Diamond, 2013). Accordingly, we questioned whether inattention would moderate the effects of specific EFs on adaptive domains and tested this hypothesis for each statistically significant EF-adaptive relation identified. However, no significant moderating effects of attention were found. This was not only surprising given the foundational role attention plays in EF processes, but also in that attention is considerably disrupted in DS (Ekstein et al., 2011). Further, links between attention and adaptive skills have been previously identified in adolescents with DS (Jacola et al., 2014), indicating even stronger evidence that attention may affect the relation between EF and adaptive behavior. The lack of significant effects in our study suggests that attentional difficulties in DS may in fact exert a moderating influence on the relation between EF and adaptive skills, but perhaps just not as measured in the present study. Given how central attention is to the primary EFs (Diamond, 2013), certain measures of attention, or EF for that matter, may pose too great a challenge in disentangling these various components in order to effectively detect their distinct roles in relation to one another.

Implications of findings for intervention targets

Considerable evidence demonstrates the malleability of EFs (Bergman Nutley et al., 2011; Diamond & Lee, 2011), and our results offer implications for improved EFs in children and adolescents with DS. Primarily, our findings provide support for shifting and working memory as potential intervention targets to improve adaptive skill outcomes. Further, the magnitude of association between working memory and all adaptive domains we examined suggests particular emphasis on improving this primary EF component could likely lead to enhanced adaptive outcomes for children and adolescents with DS. There is encouraging evidence that working memory can be improved through various types of training approaches for children and/or adolescents with DS (Conners et al., 2001; Conners et al., 2008; Sabou et al., 2012). However, much still remains to understand regarding working memory interventions in DS. For instance, there have been mixed results regarding efficacy depending on whether auditory or visuospatial modalities are targeted in training (see Conners et al., 2001). Further, the majority of research on working memory training in DS, or other populations with ID, have focused on whether training improves working memory specifically, rather than distal effects on other domains of functioning, such as adaptive behavior. Accordingly, further investigation into how EF improvement relates to additional outcomes is warranted.

Study Limitations and Future Directions

Our study provides novel insight into the role of specific EFs on communication, daily living, and socialization skills in children and adolescents with DS, though there were some limitations. Primarily, our findings are restricted to the specific characteristics of the included sample and measurement approaches. Our study also lacked a comparison group, either to typical development, or another group with a neurogenetic condition, which restricts the generalizability of our findings to some degree. Finally, our measures consisted of parent-report instruments which introduces the possibility of shared method variance contributing to effects, and may also provide a narrow assessment of the nature or degree to which EF impacts adaptive skills in DS.

Despite these few limitations, our findings provide several avenues for future research. Future work should aim towards replication of our findings with laboratory-based EF measures and also focus on identifying longitudinal age-related trends in EFs and influences on adaptive functioning. These efforts, provided findings are replicated, would further strengthen support for the evident relation between EFs and adaptive functioning identified in our study. Additionally, future work should investigate the extent to which our findings may be specific to DS, or to ID more broadly, which could inform syndrome-specific intervention targets. Finally, EF training programs, and perhaps working memory training programs more specifically, should be further tested to confirm the potential efficacy in not only improving working memory, but also targeting adaptive behavior skills for children and/or adolescents with DS.

Summary and Conclusions

Our study was a novel investigation into the role of and degree to which specific EFs influence specific aspects of adaptive behavior/functioning. We identified working memory as the most salient EF associated with adaptive functioning, as it was the only EF domain to significantly relate to communication, daily living, and socialization skills. While significant effects for shifting on some aspects of adaptive behavior were also found, our findings provide new evidence for the extended influence of working memory on yet another aspect of functioning for individuals with DS. Along with future research, these findings can contribute to improved adaptive outcomes for DS through intervention development.

Acknowledgments

This manuscript was prepared with support from the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health (F32HD097877, Will PI; L40 HD103202, Will PI; R01 HD093754, Esbensen PI) and the National Institute of Mental Health (L40 MH117727, Will PI). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This research would not have been possible without the contributions of the participating families and the community support.

Footnotes

The authors have no conflicts of interest to disclose.

References

  1. Abbeduto L, Warren SF, & Conners FA (2007). Language development in Down syndrome: From the prelinguistic period to the acquisition of literacy. Mental Retardation and Developmental Disabilities Research Reviews, 13(3), 247–261. 10.1002/mrdd.20158 [DOI] [PubMed] [Google Scholar]
  2. Amadó A, Serrat E, & Vallès-Majoral E (2016). The Role of Executive Functions in Social Cognition among Children with Down Syndrome: Relationship Patterns. Frontiers in Psychology, 7. 10.3389/fpsyg.2016.01363 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Baddeley AD, & Hitch G (1974). Working memory. Psychology of Learning and Motivation, 8, 47–49. 10.1016/S0079-7421(08)60452-1 [DOI] [Google Scholar]
  4. Baddeley A, & Jarrold C (2007). Working memory and Down syndrome. Journal of Intellectual Disability Research : JIDR, 51(Pt 12), 925–931. MEDLINE with Full Text. 10.1111/j.1365-2788.2007.00979.x [DOI] [PubMed] [Google Scholar]
  5. Bergman Nutley S, Söderqvist S, Bryde S, Thorell LB, Humphreys K, & Klingberg T (2011). Gains in fluid intelligence after training non-verbal reasoning in 4-year-old children: A controlled, randomized study. Developmental Science, 14(3), 591–601. 10.1111/j.1467-7687.2010.01022.x [DOI] [PubMed] [Google Scholar]
  6. Blair C, & Razza RP (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78(2), 647–663. MEDLINE with Full Text. 10.1111/j.1467-8624.2007.01019.x [DOI] [PubMed] [Google Scholar]
  7. Borella E, Carretti B, & Lanfranchi S (2013). Inhibitory mechanisms in Down syndrome: Is there a specific or general deficit? Research in Developmental Disabilities, 34(1), 65–71. 10.1016/j.ridd.2012.07.017 [DOI] [PubMed] [Google Scholar]
  8. Bornstein MH, Hahn C-S, & Suwalsky JTD (2013). Physically Developed and Exploratory Young Infants Contribute to Their Own Long-Term Academic Achievement. Psychological Science, 24(10), 1906–1917. 10.1177/0956797613479974 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Carney DPJ, Brown JH, & Henry LA (2013). Executive function in Williams and Down syndromes. Research in Developmental Disabilities, 34(1), 46–55. 10.1016/j.ridd.2012.07.013 [DOI] [PubMed] [Google Scholar]
  10. Cebula KR, Moore DG, & Wishart JG (2010). Social cognition in children with Down’s syndrome: Challenges to research and theory building. Journal of Intellectual Disability Research, 54(2), 113–134. 10.1111/j.1365-2788.2009.01215.x [DOI] [PubMed] [Google Scholar]
  11. Conners FA, Rosenquist CJ, Arnett L, Moore MS, & Hume LE (2008). Improving memory span in children with Down syndrome. Journal of Intellectual Disability Research, 52(3), 244–255. 10.1111/j.1365-2788.2007.01015.x [DOI] [PubMed] [Google Scholar]
  12. Conners F, Rosenquist C, & Taylor L (2001). Memory training for children with Down syndrome. Down Syndrome Research and Practice, 7(1), 25–33. 10.3104/reports.111 [DOI] [PubMed] [Google Scholar]
  13. Conners Frances A., Moore MS, Loveall SJ, & Merrill EC (2011). Memory Profiles of Down, Williams, and Fragile X Syndromes: Implications for Reading Development. Journal of Developmental & Behavioral Pediatrics, 32(5), 405–417. 10.1097/DBP.0b013e3182168f95 [DOI] [PubMed] [Google Scholar]
  14. Costanzo F, Varuzza C, Menghini D, Addona F, Gianesini T, & Vicari S (2013). Executive functions in intellectual disabilities: A comparison between Williams syndrome and Down syndrome. Research in Developmental Disabilities, 34(5), 1770–1780. 10.1016/j.ridd.2013.01.024 [DOI] [PubMed] [Google Scholar]
  15. Daunhauer L (2011). The Early Development of Adaptive Behavior and Functional Performance in Young Children with Down Syndrome: Current Knowledge and Future Directions. In International Review of Research in Developmental Disabilities (Vol. 40, pp. 109–137). Elsevier. 10.1016/B978-0-12-374478-4.00005-8 [DOI] [Google Scholar]
  16. Daunhauer LA, Fidler DJ, & Will E (2014). School Function in Students With Down Syndrome. American Journal of Occupational Therapy, 68(2), 167–176. 10.5014/ajot.2014.009274 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Daunhauer Lisa A., Fidler DJ, Hahn L, Will E, Lee NR, & Hepburn (2014). Profiles of Everyday Executive Functioning in Young Children With Down Syndrome. American Journal on Intellectual and Developmental Disabilities, 119(4), 303–318. 10.1352/1944-7558-119.4.303 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Daunhauer Lisa A., Gerlach-McDonald, Will E, & Fidler DJ (2017). Performance and Ratings Based Measures of Executive Function in School-Aged Children with Down Syndrome. Developmental Neuropsychology, 42(6), 351–368. 10.1080/87565641.2017.1360303 [DOI] [PubMed] [Google Scholar]
  19. Diamond A (2013). Executive Functions. Annual Review of Psychology, 64(1), 135–168. 10.1146/annurev-psych-113011-143750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Diamond A, & Lee K (2011). Interventions Shown to Aid Executive Function Development in Children 4 to 12 Years Old. Science, 333(6045), 959–964. 10.1126/science.1204529 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ditterline J, & Oakland T (2009). Relationships Between Adaptive Behavior and Impairment. In Naglieri J & Goldstein S (Eds.), Assessing Impairment: From Theory to Practice (pp. 31–48). Springer US. 10.1007/978-1-387-87542-2_4 [DOI] [Google Scholar]
  22. Dykens E, Hodapp R, & Evans D (1994). Profiles and development of adaptive behavior in children with Down syndrome. American Journal on Mental Retardation, 98(5), 580–587. [PubMed] [Google Scholar]
  23. Ekstein S, Glick B, Weill M, Kay B, & Berger I (2011). Down Syndrome and Attention-Deficit/Hyperactivity Disorder (ADHD). Journal of Child Neurology, 26(10), 1290–1295. 10.1177/0883073811405201 [DOI] [PubMed] [Google Scholar]
  24. Esbensen AJ, Hoffman EK, Shaffer R, Chen E, Patel L, & Jacola L (2019). Reliability of Informant-Report Measures of Executive Functioning in Children With Down Syndrome. American Journal on Intellectual and Developmental Disabilities, 124(3), 220–233. 10.1352/1944-7558-124.3.220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Esbensen AJ, Hooper SR, Fidler D, Hartley SL, Edgin J, d’Ardhuy XL, Capone G, Conners FA, Mervis CB, Abbeduto L, Rafii M, Krinsky-McHale SJ, Urv T, & Group OMW (2017). Outcome Measures for Clinical Trials in Down Syndrome. American Journal on Intellectual and Developmental Disabilities, 122(3), 247–281. 10.1352/1944-7558-122.3.247 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Evans DW, Kleinpeter FL, Slane MM, & Boomer KB (2014). Adaptive and Maladaptive Correlates of Repetitive Behavior and Restricted Interests in Persons with Down Syndrome and Developmentally-Matched Typical Children: A Two-Year Longitudinal Sequential Design. PLoS ONE, 9(4), e93951. 10.1371/journal.pone.0093951 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Fidler D, Hepburn S, & Rogers S (2006). Early learning and adaptive behaviour in toddlers with Down syndrome: Evidence for an emerging behavioural phenotype? Down Syndrome Research and Practice, 9(3), 37–44. 10.3104/reports.297 [DOI] [PubMed] [Google Scholar]
  28. Fidler DJ, Most DE, Booth-LaForce C, & Kelly JF (2008). Emerging Social Strengths in Young Children With Down Syndrome. Infants & Young Children, 21(3), 207–220. 10.1097/01.IYC.0000324550.39446.1f [DOI] [Google Scholar]
  29. Fidler DJ, Will E, Daunhauer LA, Gerlach-McDonald B, & Visootsak J (2014). Object-related generativity in children with Down syndrome. Research in Developmental Disabilities, 35(12), 3379–3385. 10.1016/j.ridd.2014.07.024 [DOI] [PubMed] [Google Scholar]
  30. Goia GA, Isquith PK, Guy SC, & Kenworthy L (2015). Brief-2: Behavior rating inventory of executive function: Professional Manual Psychological Assessment Resources. [Google Scholar]
  31. Jacola LM, Hickey F, Howe SR, Esbensen A, & Shear PK (2014). Behavior and Adaptive Functioning in Adolescents With Down Syndrome: Specifying Targets for Intervention. Journal of Mental Health Research in Intellectual Disabilities, 7(4), 287–305. 10.1080/19315864.2014.920941 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Jarrold C, Baddeley A. d, & Hewes A. k. (1999). Genetically dissociated components of working memory: Evidence from Downs and Williams syndrome. Neuropsychologia, 37(6), 637–651. 10.1016/S0028-3932(98)00128-6 [DOI] [PubMed] [Google Scholar]
  33. Jarrold C, Thorn ASC, & Stephens E (2009). The relationships among verbal short-term memory, phonological awareness, and new word learning: Evidence from typical development and Down syndrome. Journal of Experimental Child Psychology, 102(2), 196–218. 10.1016/j.jecp.2008.07.001 [DOI] [PubMed] [Google Scholar]
  34. Johnson R, & Wichern D (2007). Applied Multivariate Statistical Analysis (6th ed.). Prentice-Hall. [Google Scholar]
  35. Kasari C, & Freeman SFN (2001). Task-Related Social Behavior in Children With Down Syndrome. American Journal on Mental Retardation, 12. [DOI] [PubMed] [Google Scholar]
  36. Kittler P, Krinsky-McHale SJ, & Devenny DA (2006). Verbal intrusions precede memory decline in adults with Down syndrome. Journal of Intellectual Disability Research : JIDR, 50(Pt 1), 1–10. 10.1111/j.1365-2788.2005.00715.x [DOI] [PubMed] [Google Scholar]
  37. Lanfranchi S, Baddeley A, Gathercole S, & Vianello R (2012). Working memory in Down syndrome: Is there a dual task deficit?: Executive processing in Down syndrome. Journal of Intellectual Disability Research, 56(2), 157–166. 10.1111/j.1365-2788.2011.01444.x [DOI] [PubMed] [Google Scholar]
  38. Lanfranchi S, Cornoldi C, & Vianello R (2004). Verbal and Visuospatial Working Memory Deficits in Children With Down Syndrome. AMERICAN JOURNAL ON MENTAL RETARDATION, 109(6), 11. [DOI] [PubMed] [Google Scholar]
  39. Lanfranchi S, Jerman O, Dal Pont E, Alberti A, & Vianello R (2010). Executive function in adolescents with Down Syndrome: Executive function in Down Syndrome. Journal of Intellectual Disability Research, 54(4), 308–319. 10.1111/j.1365-2788.2010.01262.x [DOI] [PubMed] [Google Scholar]
  40. Lee NR, Anand P, Will E, Adeyemi EI, Clasen LS, Blumenthal JD, Giedd JN, Daunhauer LA, Fidler DJ, & Edgin JO (2015). Everyday executive functions in Down syndrome from early childhood to young adulthood: Evidence for both unique and shared characteristics compared to youth with sex chromosome trisomy (XXX and XXY). Frontiers in Behavioral Neuroscience, 9. 10.3389/fnbeh.2015.00264 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lee NR, Fidler DJ, Blakeley-Smith A, Daunhauer L, Robinson C, & Hepburn SL (2011). Caregiver Report of Executive Functioning in a Population-Based Sample of Young Children With Down Syndrome. American Journal on Intellectual and Developmental Disabilities, 116(4), 290–304. 10.1352/1944-7558-116.4.290 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lemons CJ, & Fuchs D (2010). Phonological awareness of children with Down syndrome: Its role in learning to read and the effectiveness of related interventions. Research in Developmental Disabilities, 31(2), 316–330. 10.1016/j.ridd.2009.11.002 [DOI] [PubMed] [Google Scholar]
  43. Loveall SJ, Conners FA, Tungate AS, Hahn LJ, & Osso TD (2017). A cross-sectional analysis of executive function in Down syndrome from 2 to 35 years: A cross-sectional analysis of executive function in Down syndrome. Journal of Intellectual Disability Research, 61(9), 877–887. 10.1111/jir.12396 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Marchal JP, Maurice-Stam H, Houtzager BA, Rutgers van Rozenburg-Marres SL, Oostrom KJ, Grootenhuis MA, & van Trotsenburg ASP (2016). Growing up with Down syndrome: Development from 6 months to 10.7 years. Research in Developmental Disabilities, 59, 437–450. 10.1016/j.ridd.2016.09.019 [DOI] [PubMed] [Google Scholar]
  45. Miyake A, & Friedman NP (2012). The Nature and Organization of Individual Differences in Executive Functions: Four General Conclusions. Current Directions in Psychological Science, 21(1), 8–14. 10.1177/0963721411429458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, & Wager TD (2000). The Unity and Diversity of Executive Functions and Their Contributions to Complex “Frontal Lobe” Tasks: A Latent Variable Analysis. Cognitive Psychology, 41(1), 49–100. 10.1006/cogp.1999.0734 [DOI] [PubMed] [Google Scholar]
  47. Næss K-AB, Lyster S-AH, Hulme C, & Melby-Lervåg M (2011). Language and verbal short-term memory skills in children with Down syndrome: A meta-analytic review. Research in Developmental Disabilities, 32(6), 2225–2234. 10.1016/j.ridd.2011.05.014 [DOI] [PubMed] [Google Scholar]
  48. Numminen H, Service E, Ahonen T, & Ruoppila I (2001). Working memory and everyday cognition in adults with Down’s syndrome. Journal of Intellectual Disability Research, 45(2), 157–168. 10.1046/j.1365-2788.2001.00298.x [DOI] [PubMed] [Google Scholar]
  49. Phillips BA, Conners FA, Merrill E, & Klinger MR (2014). Rule-Based Category Learning in Down Syndrome. American Journal on Intellectual and Developmental Disabilities, 119(3), 220–234. 10.1352/1944-7558-119.3.220 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Racz SJ, Putnick DL, Suwalsky JTD, Hendricks C, & Bornstein MH (2017). Cognitive Abilities, Social Adaptation, and Externalizing Behavior Problems in Childhood and Adolescence: Specific Cascade Effects Across Development. Journal of Youth and Adolescence, 46(8), 1688–1701. 10.1007/s10964-016-0602-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Rihtman T, Tekuzener E, Parush S, Tenenbaum A, Bachrach SJ, & Ornoy A (2010). Are the cognitive functions of children with Down syndrome related to their participation? Developmental Medicine & Child Neurology, 52(1), 72–78. 10.1111/j.1469-8749.2009.03356.x [DOI] [PubMed] [Google Scholar]
  52. Roid GH (2003). Stanford-BInet Intelligence Scales (SB-5) Riverside Publishing. [Google Scholar]
  53. Rowe J, Lavender A, & Turk V (2006). Cognitive executive function in Down’s syndrome. British Journal of Clinical Psychology, 45(1), 5–17. 10.1348/014466505X29594 [DOI] [PubMed] [Google Scholar]
  54. Sabou AM, Moldovan M, Cosma AM, & Visu-Petra L (2012). Working Memory Training in Typical and Atypical Development: Revisiting the Evidence. Cognitie, Creier, Comportament/Cognition, Brain, Behavior, 16(1), 1–47. [Google Scholar]
  55. Schalock RL, Borthwick-Duffy SA, Bradley VJ, Buntinx WHE, Coulter DL, Craig EM, Gomez SC, Lachapelle Y, Luckasson R, Reeve A, Shogren KA, Snell ME, Spreat S, Tasse MJ, Thompson JR, Verdugo-Alonso MA, Wehmeyer ML, & Yeager MH (2010). Intellectual Disability: Definition, Classification, and Systems of Supports. Eleventh Edition. In American Association on Intellectual and Developmental Disabilities American Association on Intellectual and Developmental Disabilities. [Google Scholar]
  56. Schworer EK, Esbensen AJ, Fidler DJ, Beebe DW, Carle A, & Wiley S (2021). Evaluating working memory outcome measures for children with Down syndrome. Journal of Intellectual Disability Research. Advance online publication. 10.1111/jir.12833 [DOI] [PMC free article] [PubMed]
  57. Silverman W (2007). Down syndrome: Cognitive phenotype. Mental Retardation and Developmental Disabilities Research Reviews, 13(3), 228–236. 10.1002/mrdd.20156 [DOI] [PubMed] [Google Scholar]
  58. Sparrow SS, Cicchetti DV, & Saulnier CA (2016). Vineland Adaptive Behavior Scales, 3rd Edition (Vineland-3) PsychCorp. [Google Scholar]
  59. Tassé MJ, Luckasson R, & Schalock RL (2016). The Relation Between Intellectual Functioning and Adaptive Behavior in the Diagnosis of Intellectual Disability. Intellectual and Developmental Disabilities, 54(6), 381–390. 10.1352/1934-9556-54.6.381 [DOI] [PubMed] [Google Scholar]
  60. Tomaszewski B, Fidler D, Talapatra D, & Riley K (2018). Adaptive behaviour, executive function and employment in adults with Down syndrome: Employment in adults with Down syndrome. Journal of Intellectual Disability Research, 62(1), 41–52. 10.1111/jir.12450 [DOI] [PubMed] [Google Scholar]
  61. Tungate AS, & Conners FA (2021). Executive function in Down syndrome: A meta-analysis. Research in Developmental Disabilities, 108, 103802. 10.1016/j.ridd.2020.103802 [DOI] [PubMed] [Google Scholar]
  62. Twomey C, O’Connell H, Lillis M, Tarpey SL, & O’Reilly G (2018). Utility of an abbreviated version of the stanford-binet intelligence scales (5th ed.) in estimating ‘full scale’ IQ for young children with autism spectrum disorder. Autism Research, 11(3), 503–508. 10.1002/aur.1911 [DOI] [PubMed] [Google Scholar]
  63. Van Duijn G, Dijkxhoorn Y, Scholte EM, & Van Berckelaer-Onnes IA (2010). The development of adaptive skills in young people with Down syndrome: Adaptive skills in young people with Down syndrome. Journal of Intellectual Disability Research, 54(11), 943–954. 10.1111/j.1365-2788.2010.01316.x [DOI] [PubMed] [Google Scholar]
  64. Will EA, Caravella KE, Hahn LJ, Fidler DJ, & Roberts JE (2018). Adaptive behavior in infants and toddlers with Down syndrome and fragile X syndrome. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 177(3), 358–368. 10.1002/ajmg.b.32619 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Will EA, Gerlach-McDonald B, Fidler DJ, & Daunhauer LA (2016). Impact of maladaptive behavior on school function in Down syndrome. Research in Developmental Disabilities, 59, 328–337. 10.1016/j.ridd.2016.08.018 [DOI] [PubMed] [Google Scholar]
  66. Will E, Fidler DJ, Daunhauer L, & Gerlach-McDonald B (2017). Executive function and academic achievement in primary - grade students with Down syndrome: EF and academics in Down syndrome. Journal of Intellectual Disability Research, 61(2), 181–195. 10.1111/jir.12313 [DOI] [PubMed] [Google Scholar]
  67. Wolraich ML, Lambert W, Doffing MA, Bickman L, Simmons T, & Worley K (2003). Psychometric Properties of the Vanderbilt ADHD Diagnostic Parent Rating Scale in a Referred Population. Journal of Pediatric Psychology, 28(8), 559–568. 10.1093/jpepsy/jsg046 [DOI] [PubMed] [Google Scholar]
  68. Woolf S, Woolf CM, & Oakland T (2010). Adaptive Behavior Among Adults With Intellectual Disabilities and Its Relationship to Community Independence. Intellectual and Developmental Disabilities, 48(3), 209–215. 10.1352/1944-7558-48.3.209 [DOI] [PubMed] [Google Scholar]
  69. Yang Y, Conners FA, & Merrill EC (2014). Visuo-spatial ability in individuals with Down syndrome: Is it really a strength? Research in Developmental Disabilities, 35(7), 1473–1500. 10.1016/j.ridd.2014.04.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zelazo PD (2006). The Dimensional Change Card Sort (DCCS): A method of assessing executive function in children. Nature Protocols, 1(1), 297–301. 10.1038/nprot.2006.46 [DOI] [PubMed] [Google Scholar]

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